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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Random Forest Predictive Model for ventilator-Associated Pneumonia Prediction with IoT Data Analytics

Random Forest Predictive Model for Ventilator-Associated Pne...

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2024 International conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Ramadevi, R. Kishoresonti, v.J.K. Jain Jacob, M. vaidehi, v. Mohankumar, N. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Sathyabama Institute Of Science And Technology Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Tamil Nadu Chennai India Department Of Computer Science Tamil Nadu Salem India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

To identify and forecast ventilator-associated pneumonia (vAP) early on, this study investigates the use of random forest predictive models in combination with Internet of Things (IoT) data analytics. Patients on mechanical ventilation are particularly vulnerable to vAP, a serious nosocomial infection. It uses Random Forest algorithms to examine a wide range of factors, such as vital signs, patient demographics, and ventilator settings, by using the abundance of healthcare data supplied by the IoT. The goal is to create reliable prediction models that can identify people who are at risk of getting vAP before any symptoms appear. Using data gathered from ventilator-integrated real-time monitoring devices, it creates a complete dataset. The findings reveal that random forest models are reliable and very accurate in predicting vAP. Integrating analytics from the IoT improves prediction accuracy, which in turn helps healthcare providers make better, timelier preventative decisions. Improving patient outcomes and optimizing resource allocation in critical care units may be possible as a result of this study's contributions to the expanding body of work on using sophisticated data-driven methodologies for infectious illness prediction in clinical settings. © 2024 IEEE.

关键词： Internet of things

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Assessing the Impact of Emerging Technologies on Cybersecurity with a Special Emphasis on Artificial Intelligence, the Internet of Things, and Blockchain Innovations

Assessing the Impact of Emerging Technologies on Cybersecuri...

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2024 OPJU International Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Mankhi, Eliph Mazher Rajitha, Akula Revathi, v. Thethi, H. Pal Yadav, Dinesh Kumar Kanthi, K. Surya National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India St. Martin's Engineering College Dept. of It Telangana Secunderabad India

ISBN: (纸本)9798350373783

This article proposes a safety strategy that addresses the complex concerns raised by blockchain, AI, and the Internet of Things. Use Threat Intelligence Integration (TII), Dynamic Risk Assessment (DRA), Blockchain Integrity verification (BIv), AI Adversarial Robustness Assessment (AARA), and IoT Security Compliance Assessment (ISCA). Each program is part of a larger, more linked defense system for sophisticated cyberthreats. The program uses Threat Intelligence Integration. Combining historical data with realtime hazard sources predicts assaults and their outcomes. The TII enabled new algorithms like DRA. The algorithms discover assets, assess weaknesses, and prioritize threats. Blockchain Integrity verification (BIv) checks for issues and performs complicated hash and weight computations to secure the blockchain. AI Adversarial Robustness Assessment (AARA) evaluates AI models in BIv tests and other adversarial tasks. The ISCA ensures IoT devices fulfill AARA safety and security criteria. Each algorithm prioritizes tracking, updating, and assessing to keep up with the ever-changing risk situation. The recommended solution is more accurate, finds more threats, reduces false positives, is scalable, and is simpler to set up than current defense solutions. © 2024 IEEE.

关键词： Adversarial machine learning

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Intelligent One Step Authentication Using Machine Learning Model for Secure Interaction with Electronic devices 6th

Intelligent One Step Authentication Using Machine Learning M...

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6th International conference on Soft computing and Signal Processing, ICSCSP 2023

作者： Gelli, Tharuni Mandala, Rajesh Gouri, Challa Sri Subba Rao, S.P.v. Ajitha, D. Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819984503

With the enhanced technology every process is being made very simple, especially human interaction with devices is being constantly changed. Many technologies like artificial intelligence, machine learning, blockchain, and deep learning algorithms are helping us in developing secured interactions with the devices. They are also automating the process of authentication with the help of touch less technologies. Through this paper a methodology is proposed which is user friendly to illiterates and which can be proved efficient to overcome the challenges faced by the people because of the least digital literacy. This not only provides a solution for the present but can also help our future generations in handling complex transactions in a simpler manner. In the proposed methodology, real-time machine learning model is implemented which will make the process of one step authentication secure and reliable. With the increase in IoT technologies in future the number of devices interacting with each other will be very high. In that situation this model provides the best solution in terms of implementation as well as simplicity. The results obtained during real-time implementation are presented in this paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication

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Malware Detection in Internet of Things devices Based on Association Models 23

Malware Detection in Internet of Things Devices Based on Ass...

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4th International conference on computing, Networks and Internet of Things, CNIOT 2023

作者： Quoc-Dung, Ngo Posts and Telecommunications Institute of Technology Hanoi Viet Nam

ISBN: (纸本)9798400700705

In recent years, attackers have shifted aggressively targeting Internet of Things devices. In this paper, we propose the association IoT malware detection model. Before being associated, the model goes through two processing phases, each having two types of static and dynamic features. The process consists of 3 main steps: (1) the files are extracted static feature (grayscale image) and dynamic feature (system call through v-Sandbox sandbox), (2) features are preprocessed and fed into the learning models;for the grayscale image feature, a convolutional neural network (CNN) is used;for the system call graph feature, traditional machine learning algorithms are used;(3) the results from the two learning models are combined by late fusion to decide the final prediction label for the input files. The performance of the proposed method was evaluated, and its detection accuracy was 99.14% better than in the static analysis and dynamic analysis, which had 99.06% and 98.08% detection accuracy, respectively. © 2023 ACM.

关键词： Internet of things

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Integrating Blockchain Technology for Enhanced Security and Trust in Remote IoT Healthcare Systems 3

Integrating Blockchain Technology for Enhanced Security and ...

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3rd Odisha International conference on Electrical Power Engineering, Communication and computing Technology, ODICON 2024

作者： Ellappan, v. Rajesh Sharma, R. Sungheetha, Akey Karthick, K. Karthick, S. Pradeep Ghantasala, G.S. Mahendra Institute of Technology Department of Ece Namakkal India Alliance University Department of Ece Bangalore India Alliance University Department of Computer Science and Engineering Bangalore India Gmr Institute of Technology Department of EEE Rajam India Alliance University Deaprtment of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350354379

The proposed method goals to deal with the important issues of facts privacy, personal records security, and reliability of clinical facts in allotted health care systems Blockchain generation allows decentralized structures states hand and without problems set up, disposing of risks associated with unique vulnerabilities and increasing stakeholder self-assurance. Internet of Things (IoT) devices, accepted companies, and healthcare providers all serve as nodes in a personal, permissioned blockchain network. This community guarantees the safety and integrity of data received with the aid of IoT sensors, rejecting undesirable changes. Smart contracts enable the automated execution and enforcement of facts sharing agreements, get admission to regulations, and privacy policies.... New cryptographic solutions cope with the security and reliability challenges of the Internet of Things (IoT) and healthcare structures. algorithms allow confidentiality evaluation and collaborative selection-making. This protects patient privacy and presents smooth records float and treatment. © 2024 IEEE.

关键词： Electronic health record

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Subthreshold Swing Performance Assessment of Nanoscale Junctionless GAA FETs using Machine Learning Approach 21

Subthreshold Swing Performance Assessment of Nanoscale Junct...

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21st International conference on Electrical Engineering, computing Science and Automatic Control, CCE 2024

作者： Ouchen, R. Berghout, T. Djeffal, F. Ferhati, H. University of Batna 2 LEA Batna Algeria University of Batna 2 LAME Batna Algeria University of Larbi Ben m'Hidi ISTA Oum El Bouaghi Algeria

ISBN: (纸本)9798350377545

In this work, the current-voltage (I-v) characteristics of nanoscale Junctionless Gate-All-Around (JLGAA) Field-Effect Transistor (FET) structures, with and without high-k dielectric materials, were investigated using the ATLAS 2D simulator. various machine learning (ML) algorithms were employed to analyze and classify the design factors influencing one of the principal electrical parameters of the device, whixh is the subthreshold swing (SS) factor. The SS factor is a critical parameter in evaluating the performance of nanscale Junctionless GAA FE Ts, quantifying how effectively the transistor can switch from the off state (low current) to the on state (high current). Our approach aims to study and classify the impact of oxide thickness, channel radius, channe doping and high-k dielectric materials to better understund and enhance the gate control and achieve optimal performance. It was found that the proposed design can be classified into three categories based on SS values: Good SS ( 500 mv/dec). This classification of SS values in 20 nm-Junctionless GAA FET provides a framework for understanding the trade-offs between performances and design parameters. © 2024 IEEE.

关键词： III-v semiconductors

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Artificial Intelligence Using Complex photonics: Decision Making and Reservoir computing

Artificial Intelligence Using Complex Photonics: Decision Ma...

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Optical Fiber Communication (OFC) conference

作者： Atsushi Uchida Department of Information and Computer Sciences Saitama University Saitama City Saitama Japan

We overview recent development on photonic decision making and reservoir computing for artificial intelligence using complex photonics. Parallel implementations of photonic devices can accelerate information processin... 详细信息

ISBN: (纸本)9781957171180

关键词： Machine learning algorithms Chaotic communication Semiconductor lasers Scalability Decision making Reservoirs Laser modes

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Recurrent Neural Network for M/EEG Source Localization 24

Recurrent Neural Network for M/EEG Source Localization

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24th IEEE International conference of Young Professionals in Electron devices and Materials, EDM 2023

作者： vakhitov, Rodion R. Khlopkov, Artem D. Lukin, Alexander S. Toma, vladislav v. Snytkin, Alexander E. Kulikov, Roman S. Moscow Power Engineering Institute Computing Machines Systems and Networks Moscow Russia Moscow Power Engineering Institute Biotechnical and Medical Devices and Systems Moscow Russia Moscow Power Engineering Institute Radio Systems Moscow Russia

ISBN: (纸本)9798350336870

This paper presents the solution of the inverse M/EEG problem using machine learning algorithms. The data on which the algorithm was trained were obtained as a result of solving the forward problem. The data are generated using a recurrent model of neuronal brain activity, where the consecutive excitation of sources in time of closely spaced sources is based on a stochastic Markov chain. The dipole activation model is expected to increase the accuracy of the M/EEG inverse problem solution by reducing its indeterminacy by replacing uncorrelated excitations of sources whose magnetic field is statically distributed on the brain surface with their spatially related time series. Machine learning algorithms in this case can recognize the correlation of source excitations and solve the problem in real time. The application of a method for localization of neuronal activity of the brain that takes into account the spatiotemporal character of the excitations can presumably provide a more complete picture of the brain functioning. © 2023 IEEE.

关键词： Recurrent neural networks

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Experience Replay: A value-based deep reinforcement approach for Malicious URL Detection in the Tor Browser 7

Experience Replay: A value-based deep reinforcement approach...

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7th International conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Sakthivel, K. Srinithi, v. Swetha, S. Birundha, S. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372816

Finding malicious URLs in Tor is vital for preserving network security and privacy. Subsequently, Tor affords anonymity by routing traffic over a sequence of relays, it's frequently used for both legitimate and illegitimate reasons. Malicious URLs can lead to various threats, including phishing, malware distribution and illegal activities. Malicious URLs can lead users to websites hosting malware, which can compromise their devices and steal sensitive information. This article explores the discovery of malicious URLs through the ML and DL concepts. By utilizing the techniques such as Correlation-based Feature Selection, Chi-Square Test, Principal Component Analysis and Linear Discriminant Analysis, key features which are contributing to URL detection can be identified. Implementation of KNN, K Means, Random Forest, DQN and DDQN algorithms offers intuitions into how malicious URLs are recognized and alleviates possible cyber threats efficiently. The objective of this work includes, the idea of applying the deep reinforcement learning methods in which can pave the way to surge the accuracy of detection of the URL. It also stretches an idea for employing the deep reinforcement learning techniques for the chosen dataset. © 2024 IEEE.

关键词： Phishing

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Remote Monitoring and Control of Agricultural Systems Using IoT and Machine Learning 3

Remote Monitoring and Control of Agricultural Systems Using ...

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International conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Raj, G. Bhupal Mohan, Chinnem Rama Karthik, A. Nagpal, Amandeep Laxmi, Ms vijay Asha, v SRUniv Sch Agr Warangal Telangana India Narayana Engn Coll Dept Comp Sci & Engn Nellore 524004 Andhra Pradesh India Inst Aeronaut Engn Hyderabad India Lovely Profess Univ Phagwara India Maharishi Markandeshwar Univ Ambala India New Horizon Coll Engn Dept Comp Applicat Bangalore Karnataka India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Cutting-edge technology is essential to improve resource use, output, and farming methods to meet climate change and other needs. This research uses IoT and machine learning to remotely track and operate agricultural *** Internet of Things (IoT) lets several sensors strategically placed around the farm collect data simultaneously. These sensors assess temperature, humidity, and soil wetness, which determine crop health. The data is wirelessly delivered from a central location to a designated area for processing and *** learning is used to interpret data. These algorithms can improve irrigation schedules, predict food yields, diagnose diseases, and offer insect control alternatives. Machine learning (ML) models improve by learning from data and adapting to environmental *** system allows remote farming monitoring using actuators and automated tools. An easy-to-use interface on desktops or mobile devices allows agricultural experts utilise machine learning models to regulate insecticides, modify irrigation water, and start harvesting *** proposed technology improves farming productivity, durability, resource efficiency, and cost. It encourages farmers to make sensible decisions and manage their resources to better address environmental *** conclude, Machine Learning and the Internet of Things could considerably improve farming system remote monitoring and control. The development of new technologies in this field is crucial to its long-term success and ability to provide for future generations.

关键词： Remote Monitoring Control Systems Agricultural IoT Precision Agriculture Machine Learning Sensor Networks Data Analytics

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